MSGSU
ISTATISTIK BOLUMU - R ILE ISTATISTIKSEL PROGRAMLAMA DERS NOTLARI
by ozge.ozdamar@msgsu.edu.tr is licensed under a
Creative
Commons Attribution-NonCommercial-ShareAlike 4.0 International
License.
Hata ve öneriler için emoji::("email")
The most important feature of the R graphics setup is the existence of two distinct graphics systems within R:
1 . Traditional graphics system : High level functions
library(graphics)
2 . Grid graphics system : Unique to R and is much more powerful than the traditional system. Low level functions
library(grid)
# Deepayan Sarkar’s lattice package, based on Bill Cleveland’s Trellis graphics paradigm.
library(lattice)
# Hadley Wickhams’ ggplot2, based on Leland Wilkinson’s Grammar of Graphics paradigm.
library(ggplot2)
Underlying both traditional and grid graphics systems is a graphics engine, which represents a common set of fundamental graphics facilities, such as color management and support for different graphical output formats.
plot() function is a “smart” (generic) command. It
means that plot() “understands” the type of the supplied
object, and draws accordingly.# vector
plot(mtcars$disp)
# data.frame
plot(mtcars)
# model
plot(lm(mtcars$mpg ~ mtcars$disp, data=mtcars))
plot(mtcars$disp, type="n")
mtext("Title", line=1.5, font=2)
points(mtcars$disp)
legend("topleft", pch=1, legend="My wonderful points")
When you enter plot(), R opens screen graphical device
and starts to draw there.
If the next command is of the same type, R will erase the content of the device and start the new plot.
If the next command is the “adding” one, like text(), R
will add something to the existing plot.
If the next command is dev.off(), R will close the
device.
When needed more than one graphical window, open additional device
with dev.new()
png(file="deneme.png", bg="transparent")
# pdf(file="deneme.pdf", width=8) # default 7 inch
# svg(file="deneme.svg")
plot(1:20)
text(10, 20, "a")
dev.off()
## quartz_off_screen
## 2
basic graphics for 1 variable
basic graphics for 2 variables
basic graphics for multi variables
plot()type
plot(mtcars$mpg) #vector
plot(mtcars$mpg,type="p")
plot(mtcars$mpg,type="l")
point types : pch
plot(mtcars$mpg, type="p",pch=8)
plot(mtcars$mpg, type="p",pch=19)
plot(mtcars$mpg,
type="p",pch=22,col="blue",bg="red")
plot(mtcars$mpg,
type="p",pch="R")
color
We can specify the colors through names (or index numbers), hexadecimal, rgb, hsv * Names - col=“red” or 558 * Hex - col=“#FFFFFF” * rgb - col=rgb(1,3,1) * hsv - col=hsv(0,0,3)
col - for specifying the default plotting color. col=c(“red”,“blue”,“green”) * col.axis:* color for the axis text * col.lab:* color for the axis label * col.main:* color of the main title * col.sub:* color of the subtitle * fg:* color of the foreground * bg:* color of the background
colors() # 657 renk colours()
## [1] "white" "aliceblue" "antiquewhite"
## [4] "antiquewhite1" "antiquewhite2" "antiquewhite3"
## [7] "antiquewhite4" "aquamarine" "aquamarine1"
## [10] "aquamarine2" "aquamarine3" "aquamarine4"
## [13] "azure" "azure1" "azure2"
## [16] "azure3" "azure4" "beige"
## [19] "bisque" "bisque1" "bisque2"
## [22] "bisque3" "bisque4" "black"
## [25] "blanchedalmond" "blue" "blue1"
## [28] "blue2" "blue3" "blue4"
## [31] "blueviolet" "brown" "brown1"
## [34] "brown2" "brown3" "brown4"
## [37] "burlywood" "burlywood1" "burlywood2"
## [40] "burlywood3" "burlywood4" "cadetblue"
## [43] "cadetblue1" "cadetblue2" "cadetblue3"
## [46] "cadetblue4" "chartreuse" "chartreuse1"
## [49] "chartreuse2" "chartreuse3" "chartreuse4"
## [52] "chocolate" "chocolate1" "chocolate2"
## [55] "chocolate3" "chocolate4" "coral"
## [58] "coral1" "coral2" "coral3"
## [61] "coral4" "cornflowerblue" "cornsilk"
## [64] "cornsilk1" "cornsilk2" "cornsilk3"
## [67] "cornsilk4" "cyan" "cyan1"
## [70] "cyan2" "cyan3" "cyan4"
## [73] "darkblue" "darkcyan" "darkgoldenrod"
## [76] "darkgoldenrod1" "darkgoldenrod2" "darkgoldenrod3"
## [79] "darkgoldenrod4" "darkgray" "darkgreen"
## [82] "darkgrey" "darkkhaki" "darkmagenta"
## [85] "darkolivegreen" "darkolivegreen1" "darkolivegreen2"
## [88] "darkolivegreen3" "darkolivegreen4" "darkorange"
## [91] "darkorange1" "darkorange2" "darkorange3"
## [94] "darkorange4" "darkorchid" "darkorchid1"
## [97] "darkorchid2" "darkorchid3" "darkorchid4"
## [100] "darkred" "darksalmon" "darkseagreen"
## [103] "darkseagreen1" "darkseagreen2" "darkseagreen3"
## [106] "darkseagreen4" "darkslateblue" "darkslategray"
## [109] "darkslategray1" "darkslategray2" "darkslategray3"
## [112] "darkslategray4" "darkslategrey" "darkturquoise"
## [115] "darkviolet" "deeppink" "deeppink1"
## [118] "deeppink2" "deeppink3" "deeppink4"
## [121] "deepskyblue" "deepskyblue1" "deepskyblue2"
## [124] "deepskyblue3" "deepskyblue4" "dimgray"
## [127] "dimgrey" "dodgerblue" "dodgerblue1"
## [130] "dodgerblue2" "dodgerblue3" "dodgerblue4"
## [133] "firebrick" "firebrick1" "firebrick2"
## [136] "firebrick3" "firebrick4" "floralwhite"
## [139] "forestgreen" "gainsboro" "ghostwhite"
## [142] "gold" "gold1" "gold2"
## [145] "gold3" "gold4" "goldenrod"
## [148] "goldenrod1" "goldenrod2" "goldenrod3"
## [151] "goldenrod4" "gray" "gray0"
## [154] "gray1" "gray2" "gray3"
## [157] "gray4" "gray5" "gray6"
## [160] "gray7" "gray8" "gray9"
## [163] "gray10" "gray11" "gray12"
## [166] "gray13" "gray14" "gray15"
## [169] "gray16" "gray17" "gray18"
## [172] "gray19" "gray20" "gray21"
## [175] "gray22" "gray23" "gray24"
## [178] "gray25" "gray26" "gray27"
## [181] "gray28" "gray29" "gray30"
## [184] "gray31" "gray32" "gray33"
## [187] "gray34" "gray35" "gray36"
## [190] "gray37" "gray38" "gray39"
## [193] "gray40" "gray41" "gray42"
## [196] "gray43" "gray44" "gray45"
## [199] "gray46" "gray47" "gray48"
## [202] "gray49" "gray50" "gray51"
## [205] "gray52" "gray53" "gray54"
## [208] "gray55" "gray56" "gray57"
## [211] "gray58" "gray59" "gray60"
## [214] "gray61" "gray62" "gray63"
## [217] "gray64" "gray65" "gray66"
## [220] "gray67" "gray68" "gray69"
## [223] "gray70" "gray71" "gray72"
## [226] "gray73" "gray74" "gray75"
## [229] "gray76" "gray77" "gray78"
## [232] "gray79" "gray80" "gray81"
## [235] "gray82" "gray83" "gray84"
## [238] "gray85" "gray86" "gray87"
## [241] "gray88" "gray89" "gray90"
## [244] "gray91" "gray92" "gray93"
## [247] "gray94" "gray95" "gray96"
## [250] "gray97" "gray98" "gray99"
## [253] "gray100" "green" "green1"
## [256] "green2" "green3" "green4"
## [259] "greenyellow" "grey" "grey0"
## [262] "grey1" "grey2" "grey3"
## [265] "grey4" "grey5" "grey6"
## [268] "grey7" "grey8" "grey9"
## [271] "grey10" "grey11" "grey12"
## [274] "grey13" "grey14" "grey15"
## [277] "grey16" "grey17" "grey18"
## [280] "grey19" "grey20" "grey21"
## [283] "grey22" "grey23" "grey24"
## [286] "grey25" "grey26" "grey27"
## [289] "grey28" "grey29" "grey30"
## [292] "grey31" "grey32" "grey33"
## [295] "grey34" "grey35" "grey36"
## [298] "grey37" "grey38" "grey39"
## [301] "grey40" "grey41" "grey42"
## [304] "grey43" "grey44" "grey45"
## [307] "grey46" "grey47" "grey48"
## [310] "grey49" "grey50" "grey51"
## [313] "grey52" "grey53" "grey54"
## [316] "grey55" "grey56" "grey57"
## [319] "grey58" "grey59" "grey60"
## [322] "grey61" "grey62" "grey63"
## [325] "grey64" "grey65" "grey66"
## [328] "grey67" "grey68" "grey69"
## [331] "grey70" "grey71" "grey72"
## [334] "grey73" "grey74" "grey75"
## [337] "grey76" "grey77" "grey78"
## [340] "grey79" "grey80" "grey81"
## [343] "grey82" "grey83" "grey84"
## [346] "grey85" "grey86" "grey87"
## [349] "grey88" "grey89" "grey90"
## [352] "grey91" "grey92" "grey93"
## [355] "grey94" "grey95" "grey96"
## [358] "grey97" "grey98" "grey99"
## [361] "grey100" "honeydew" "honeydew1"
## [364] "honeydew2" "honeydew3" "honeydew4"
## [367] "hotpink" "hotpink1" "hotpink2"
## [370] "hotpink3" "hotpink4" "indianred"
## [373] "indianred1" "indianred2" "indianred3"
## [376] "indianred4" "ivory" "ivory1"
## [379] "ivory2" "ivory3" "ivory4"
## [382] "khaki" "khaki1" "khaki2"
## [385] "khaki3" "khaki4" "lavender"
## [388] "lavenderblush" "lavenderblush1" "lavenderblush2"
## [391] "lavenderblush3" "lavenderblush4" "lawngreen"
## [394] "lemonchiffon" "lemonchiffon1" "lemonchiffon2"
## [397] "lemonchiffon3" "lemonchiffon4" "lightblue"
## [400] "lightblue1" "lightblue2" "lightblue3"
## [403] "lightblue4" "lightcoral" "lightcyan"
## [406] "lightcyan1" "lightcyan2" "lightcyan3"
## [409] "lightcyan4" "lightgoldenrod" "lightgoldenrod1"
## [412] "lightgoldenrod2" "lightgoldenrod3" "lightgoldenrod4"
## [415] "lightgoldenrodyellow" "lightgray" "lightgreen"
## [418] "lightgrey" "lightpink" "lightpink1"
## [421] "lightpink2" "lightpink3" "lightpink4"
## [424] "lightsalmon" "lightsalmon1" "lightsalmon2"
## [427] "lightsalmon3" "lightsalmon4" "lightseagreen"
## [430] "lightskyblue" "lightskyblue1" "lightskyblue2"
## [433] "lightskyblue3" "lightskyblue4" "lightslateblue"
## [436] "lightslategray" "lightslategrey" "lightsteelblue"
## [439] "lightsteelblue1" "lightsteelblue2" "lightsteelblue3"
## [442] "lightsteelblue4" "lightyellow" "lightyellow1"
## [445] "lightyellow2" "lightyellow3" "lightyellow4"
## [448] "limegreen" "linen" "magenta"
## [451] "magenta1" "magenta2" "magenta3"
## [454] "magenta4" "maroon" "maroon1"
## [457] "maroon2" "maroon3" "maroon4"
## [460] "mediumaquamarine" "mediumblue" "mediumorchid"
## [463] "mediumorchid1" "mediumorchid2" "mediumorchid3"
## [466] "mediumorchid4" "mediumpurple" "mediumpurple1"
## [469] "mediumpurple2" "mediumpurple3" "mediumpurple4"
## [472] "mediumseagreen" "mediumslateblue" "mediumspringgreen"
## [475] "mediumturquoise" "mediumvioletred" "midnightblue"
## [478] "mintcream" "mistyrose" "mistyrose1"
## [481] "mistyrose2" "mistyrose3" "mistyrose4"
## [484] "moccasin" "navajowhite" "navajowhite1"
## [487] "navajowhite2" "navajowhite3" "navajowhite4"
## [490] "navy" "navyblue" "oldlace"
## [493] "olivedrab" "olivedrab1" "olivedrab2"
## [496] "olivedrab3" "olivedrab4" "orange"
## [499] "orange1" "orange2" "orange3"
## [502] "orange4" "orangered" "orangered1"
## [505] "orangered2" "orangered3" "orangered4"
## [508] "orchid" "orchid1" "orchid2"
## [511] "orchid3" "orchid4" "palegoldenrod"
## [514] "palegreen" "palegreen1" "palegreen2"
## [517] "palegreen3" "palegreen4" "paleturquoise"
## [520] "paleturquoise1" "paleturquoise2" "paleturquoise3"
## [523] "paleturquoise4" "palevioletred" "palevioletred1"
## [526] "palevioletred2" "palevioletred3" "palevioletred4"
## [529] "papayawhip" "peachpuff" "peachpuff1"
## [532] "peachpuff2" "peachpuff3" "peachpuff4"
## [535] "peru" "pink" "pink1"
## [538] "pink2" "pink3" "pink4"
## [541] "plum" "plum1" "plum2"
## [544] "plum3" "plum4" "powderblue"
## [547] "purple" "purple1" "purple2"
## [550] "purple3" "purple4" "red"
## [553] "red1" "red2" "red3"
## [556] "red4" "rosybrown" "rosybrown1"
## [559] "rosybrown2" "rosybrown3" "rosybrown4"
## [562] "royalblue" "royalblue1" "royalblue2"
## [565] "royalblue3" "royalblue4" "saddlebrown"
## [568] "salmon" "salmon1" "salmon2"
## [571] "salmon3" "salmon4" "sandybrown"
## [574] "seagreen" "seagreen1" "seagreen2"
## [577] "seagreen3" "seagreen4" "seashell"
## [580] "seashell1" "seashell2" "seashell3"
## [583] "seashell4" "sienna" "sienna1"
## [586] "sienna2" "sienna3" "sienna4"
## [589] "skyblue" "skyblue1" "skyblue2"
## [592] "skyblue3" "skyblue4" "slateblue"
## [595] "slateblue1" "slateblue2" "slateblue3"
## [598] "slateblue4" "slategray" "slategray1"
## [601] "slategray2" "slategray3" "slategray4"
## [604] "slategrey" "snow" "snow1"
## [607] "snow2" "snow3" "snow4"
## [610] "springgreen" "springgreen1" "springgreen2"
## [613] "springgreen3" "springgreen4" "steelblue"
## [616] "steelblue1" "steelblue2" "steelblue3"
## [619] "steelblue4" "tan" "tan1"
## [622] "tan2" "tan3" "tan4"
## [625] "thistle" "thistle1" "thistle2"
## [628] "thistle3" "thistle4" "tomato"
## [631] "tomato1" "tomato2" "tomato3"
## [634] "tomato4" "turquoise" "turquoise1"
## [637] "turquoise2" "turquoise3" "turquoise4"
## [640] "violet" "violetred" "violetred1"
## [643] "violetred2" "violetred3" "violetred4"
## [646] "wheat" "wheat1" "wheat2"
## [649] "wheat3" "wheat4" "whitesmoke"
## [652] "yellow" "yellow1" "yellow2"
## [655] "yellow3" "yellow4" "yellowgreen"
plot(mtcars$mpg,
type="p",pch=19,col="red")
plot(mtcars$mpg,
type="p",pch=19,col="27")
plot(mtcars$mpg,
type="p",pch=19,col="red", fg="blue")
plot(mtcars$mpg,
type="p",pch=19,col="blue")
size: cex
plot(mtcars$mpg,type="p",
pch=19,col="red",cex=1)
plot(mtcars$mpg,type="p",
pch=19,col="red",cex=2)
line type: lyt
plot(mtcars$mpg,type="l",pch=19,
col="red",cex=2, lty=2)
plot(mtcars$mpg,type="l",pch=19,
col="red",cex=2, lty=4)
lwd The width of lines is specified by a simple numeric value, e.g., lwd=3. This value is a multiple of 1/96 inch, with a lower limit of 1 pixel on some screen devices. The default value is 1. lwd : line width relative to the default (default=1). 2 is twice as wide
plot(mtcars$mpg,type="l",pch=19,
col="red",cex=2, lty=2,lwd=2)
plot(mtcars$mpg,type="l",pch=19,
col="red",cex=2, lty=2,lwd=4)
box type : bty - o : default - l - 7 - c - u - ]
plot(mtcars$mpg,type="l",pch=19,
col="red",cex=2, lty=2,lwd=3,
bty="u")
plot(mtcars$mpg,type="l",pch=19,
col="red",cex=2, lty=2,lwd=3,
bty="7")
plot(mtcars$mpg,type="l",pch=19,col="red",cex=2, lty=2,lwd=3,
main="GRAFIK BASLIGI",
xlab = "X EKSENI", ylab = "Y EKSENI",
col.main="blue",
col.lab="darkgreen",
cex.main=3, # baslik buyuklugu
cex.lab=1.5, # eksen isim buyuklugu
xlim = c(0,50), # x eksen genisligi
ylim=c(5,40) # y eksen genisligi
)
?par # for more graphical parameters
par(mfrow=c(2,2))
plot(mtcars$mpg, type="p",pch=19,col="red", main="A")
plot(mtcars$mpg, type="p",pch=19,col="blue", main = "B")
plot(mtcars$mpg, type="p",pch=19, col="darkgreen", main = "C")
plot(mtcars$mpg, type="p",pch=19, col="orange", main = "D")
simple bar plot
counts1<-table(mtcars$gear)
counts1
##
## 3 4 5
## 15 12 5
barplot(counts1, main="Simple Bar Plot")
Simple Bar Plot with Labels + color
barplot(counts1, main="Simple Bar Plot",xlab="Gear", ylab="Frequency") # add axis labels
barplot(counts1, main="Simple Bar Plot",
xlab="Gear",
ylab="Frequency",
col="lightblue") # add color
barplot(counts1, main="Simple Bar Plot",
xlab="Gear",
ylab="Frequency",
col= hcl(seq(0, 240, by = 60))) # tricolored
barplot(counts1, main="Simple Bar Plot",
xlab="Gear", ylab="Frequency",
names.arg=c("3 Gear", "4 Gear", "5 Gear")) #change labels
simple horizontal bar plot
barplot(counts1, main="Treatment Outcome",
horiz=TRUE, cex.names=0.8,
cex.main=1.5, las=1,xlim= c(0,50),
names.arg=c("3 Gear", "4 Gear","5 Gear"))
barplot(counts1, main="Simple Horizontal Bar Plot",xlab="Improvement",
ylab="Frequency",horiz=T,col=hcl(seq(0, 240, by = 60)))
If the categorical variable to be plotted is a factor or ordered factor, you can create a vertical bar plot quickly with the plot() function.
mtcars1<-mtcars
mtcars1$gear<-as.factor(mtcars$gear)
str(mtcars1)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: Factor w/ 3 levels "3","4","5": 2 2 2 1 1 1 1 2 2 2 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
plot(mtcars1$gear, main="Simple Bar Plot", xlab="Improved", ylab="Frequency")
stacked bar plot
counts2 <- table(mtcars$vs, mtcars$gear)
counts2
##
## 3 4 5
## 0 12 2 4
## 1 3 10 1
barplot(counts2, main="Stacked Bar Plot",
xlab="GEAR", col=c("darkblue","red"),
legend = rownames(counts2))
legend adjustment ref
grouped bar plot
barplot(counts2, main="Grouped Bar Plot",
xlab="Gear", ylab="Frequency",
col=c("darkblue","red"), beside=TRUE)
legend("topleft", legend=rownames(counts2),
fill=c("darkblue","red"), horiz=FALSE, title="VS")
barplot(counts2, main="Grouped Bar Plot",
xlab="Gear", ylab="Frequency",
col=c("darkblue","red"), beside=TRUE, legend.text=c("no vs","1 vs")) # legend text
Spinograms
# vcd::spine
# graphics:spineplot
counts2
##
## 3 4 5
## 0 12 2 4
## 1 3 10 1
vcd::spine(counts2, main = "Spinogram")
graphics::spineplot(counts2, main = "Spinogram")
hist(mtcars$mpg, breaks = "Sturges")
hist(mtcars$mpg, breaks = "Scott")
hist(mtcars$mpg, breaks = "FD") # = "Freedman-Diaconis"
hist(mtcars$mpg, col = "steelblue",breaks = 30)
tit="Galon Başına Mil Histogramı"
xl= "Galon Başına Mil"
yl="Frekans"
hist(mtcars$mpg, main = tit, xlab = xl, ylab = yl,
col = "orange", border = " red", col.lab= " blue")
plot(density(mtcars$mpg), frame = FALSE, col = "steelblue", main = "Density plot of mpg")
# Compute the density data
dens <- density(mtcars$mpg)
# plot density
# Fill the density plot using polygon()
plot(density(mtcars$mpg), frame = FALSE, col = "steelblue",
main = "Density plot of mpg")
polygon(density(mtcars$mpg), col = "steelblue")
library(MASS)
MASS::truehist(mtcars$mpg)
boxplot(mtcars$mpg)
boxplot(mtcars$mpg,horizontal = TRUE)
boxplot(mtcars$mpg, notch = TRUE)
# In the notched boxplot, if two boxes' notches do not overlap this
# is ‘strong evidence’ their medians differ
boxplot(mpg~cyl,data = mtcars, main = "Silindire göre mil",
xlab = "Silindir sayısı", ylab = "Mil", varwidth=TRUE)
# make boxplot widths proportional to the square root of the samples sizes
par(mfrow=c(2,1))
boxplot(mpg~cyl, data = mtcars, varwidth = FALSE)
boxplot(mpg~cyl, data = mtcars, varwidth = TRUE)
boxplot(mpg~cyl,data = mtcars, varwidth = TRUE, horizontal = TRUE)
pie(counts1)
pie(counts1, labels = c("A","B","C")) # add labels
library(plotrix)
x = c(30,25,28,10,7)
lbl = c("A","B","C","D","E")
plotrix::fan.plot(x, labels = lbl, col = rainbow(5),ticks = 30, main = "Fan Plot")
fancol = c("burlywood","turquoise","firebrick","gold3","green4")
#compare with pie
pie(x,labels=lbl,col=fancol)
plotrix::fan.plot(x, labels = lbl, col = fancol,ticks = 180,max.span=pi, main = "Fan Plot") # maximum span <= 2*pi
# library(vioplot)
x1 <- mtcars$mpg[mtcars$cyl==4]
x2 <- mtcars$mpg[mtcars$cyl==6]
x3 <- mtcars$mpg[mtcars$cyl==8]
vioplot::vioplot(x1, x2, x3, names=c("4 cyl", "6 cyl", "8 cyl"), col="green")
#title("Violin Plot")
# box vs violin
par(mfrow=c(2,1))
mu<-2
si<-0.6
bimodal<-c(rnorm(1000,-mu,si),rnorm(1000,mu,si))
uniform<-runif(2000,-4,4)
normal<-rnorm(2000,0,3)
#vioplot::vioplot(bimodal,uniform,normal)
boxplot(bimodal,uniform,normal)
plot(mtcars$wt, mtcars$mpg, type= "p", col= "red",
main="Scatterplot Örneği",
xlab="Araç Ağırlığı", ylab="Galon Başına Mil", pch=24)
# fit line
linreg<-lm(mpg~wt, data = mtcars)
plot(mtcars$wt, mtcars$mpg)
abline(linreg, col = "red")
# regression line (y~x)
lines(lowess(mtcars$wt,mtcars$mpg), col="blue")
# lowess line (x,y)
# pairs
pairs(~mpg + disp + drat + wt, data = mtcars,
main=" Scatterplot Matrisi")
# par()
par(mfrow=c(2,2)) # mfrow, mfcol
plot(mtcars$wt,mtcars$mpg, main="Scatterplot of wt vs. mpg")
plot(mtcars$wt,mtcars$disp, main="Scatterplot of wt vs disp")
hist(mtcars$wt, main="Histogram of wt")
boxplot(mtcars$wt, main="Boxplot of wt")
# layout
layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE))
hist(mtcars$wt)
hist(mtcars$mpg)
hist(mtcars$disp)
OYKK2020-R
ile Veri Önişleme by ozge.ozdamar@msgsu.edu.tr is licensed under a
Creative
Commons Attribution-NonCommercial-ShareAlike 4.0 International
License.